Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam

This repository accompanies our study on non-destructive sugar content estimation in apple jam using VNIR hyperspectral imaging (HSI) and machine learning. It includes a reproducible set of Jupyter notebooks covering preprocessing, dataset construction, and model training/evaluation with classical ML and deep learning.

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Dataset

The Apples_HSI dataset is available on Hugging Face: issai/Apples_HSI.

Dataset structure

Apples_HSI/
├── Catalogs/                                    # per-cultivar & sugar-ratio sessions
│   ├── apple_jam_{cultivar}_{sugar proportion}_{apple proportion}_{date}/   # e.g., apple_jam_gala_50_50_17_Dec
│   │   ├── {sample_id}/                         # numeric sample folders (e.g., 911, 912, …)
│   │   │   ├── capture/                         # raw camera outputs + references
│   │   │   │   ├── {sample_id}.raw              # raw hyperspectral cube
│   │   │   │   ├── {sample_id}.hdr              # header/metadata for the raw cube
│   │   │   │   ├── DARKREF_{sample_id}.raw      # dark reference (raw)
│   │   │   │   ├── DARKREF_{sample_id}.hdr
│   │   │   │   ├── WHITEREF_{sample_id}.raw     # white reference (raw)
│   │   │   │   └── WHITEREF_{sample_id}.hdr
│   │   │   ├── metadata/
│   │   │   │   └── {sample_id}.xml              # per-sample metadata/annotations
│   │   │   ├── results/                         # calibrated reflectance + previews
│   │   │   │   ├── REFLECTANCE_{sample_id}.dat  # ENVI-style reflectance cube
│   │   │   │   ├── REFLECTANCE_{sample_id}.hdr
│   │   │   │   ├── REFLECTANCE_{sample_id}.png  # reflectance preview
│   │   │   │   ├── RGBSCENE_{sample_id}.png     # RGB scene snapshot
│   │   │   │   ├── RGBVIEWFINDER_{sample_id}.png
│   │   │   │   └── RGBBACKGROUND_{sample_id}.png
│   │   │   ├── manifest.xml                     # per-sample manifest
│   │   │   ├── {sample_id}.png                  # sample preview image
│   │   │   └── .validated                       # empty marker file
│   │   └── …                                    # more samples
│   └── …                                        # more cultivar/ratio/date folders
│
├── .cache/                                      # service files (upload tool)
├── ._.cache
├── ._paths.rtf
├── .gitattributes                               # LFS rules for large files
└── paths.rtf                                    # path list (RTF)

Repository structure

This repository contains:

• Pre-processing: 1_preprocessing.ipynb (import HSI, calibration, masking (SAM), ROI crop, grid subdivision).
• Dataset building: 2_dataset preparation.ipynb (train/val/test splits, sugar concentration/apple cultivar splits, average spectral vectors extraction).
• Model training & evaluation:
  • 3_svm.ipynb — SVM, scaling, hyperparameter search.
  • 4_xgboost.ipynb — XGBoost, tuning & early stopping.
  • 5_resnet.ipynb — 1D ResNet training loops, checkpoints, metrics.

Preprocessing → Dataset → Models (How to Run)

1) Preprocessing

Inputs to set (near the bottom of the notebook)

input_root = "path/to/input"     # root that contains the dataset folders (e.g., Apples_HSI/Catalogs)
output_root = "path/to/output"   # where the NPZ files will be written
paths_txt = "path/to/paths.txt"  # text file with relative paths to .hdr files (one per line)

• Run all cells. The notebook:

  • reads REFLECTANCE_*.hdr with spectral.open_image
  • builds a SAM mask (ref pixel (255, 247), threshold 0.19)
  • crops ROI and saves cropped_{ID}.npz under output_root/...

• Each NPZ contains: cube (cropped H×W×Bands), offset (y_min, x_min), metadata (JSON).

2) Dataset building

Run all cells. The notebook:

• loads each NPZ (np.load(path)["cube"])
• extracts mean spectra per patch for grid sizes 1, ..., 5
• creates tables with columns band_0..band_(B-1), apple_content, apple_type
• writes splits per grid:
  • apple-based: {g}x{g}_train_apple.csv, {g}x{g}_val_apple.csv, {g}x{g}_test_apple.csv
  • rule-based: {g}x{g}_train_rule.csv, {g}x{g}_val_rule.csv, {g}x{g}_test_rule.csv

3) Model training

Classical ML — 3_svm.ipynb Run all cells. The notebook:

• loads pre-split CSVs (e.g., {g}x{g}_train_apple.csv, {g}x{g}_test_apple.csv)
• scales inputs and targets with MinMaxScaler
• fits SVR with hyperparameters: C=110, epsilon=0.2, gamma="scale"
• reports RMSE / MAE / R² on Train/Test (targets inverse-transformed)

Classical ML — 4_xgboost.ipynb Run all cells. The notebook:

• loads Train/Val/Test CSVs and scales inputs with MinMaxScaler
• builds DMatrix and trains with: objective = "reg:squarederror", eval_metric = "rmse", max_depth = 2, eta = 0.15, subsample = 0.8, colsample_bytree = 1.0, lambda = 2.0, alpha = 0.1, seed = 42 num_boost_round = 400, early_stopping_rounds = 40
• evaluates and prints RMSE / MAE / R² (Train/Test)

Deep model — 5_resnet.ipynb Run all cells. The notebook:

• builds a ResNet1D and DataLoaders (batch_size=16)
• trains with Adam (lr=1e-3, weight_decay=1e-4), epochs=150, MAE loss
• uses target MinMaxScaler (inverse-transforms predictions for metrics)
• early-stopping on Val MAE; saves best checkpoint to best_resnet1d_model.pth
• reports RMSE / MAE / R² on the Test set

If you use the dataset/source code/pre-trained models in your research, please cite our work:

Lissovoy, D., Zakeryanova, A., Orazbayev, R., Rakhimzhanova, T., Lewis, M., Varol, H. A., & Chan, M.-Y. (2025). Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam. Foods, 14(21), 3585. https://doi.org/10.3390/foods14213585